System and method for machine based medical diagnostic code identification, accumulation, analysis and automatic claim process adjudication

ABSTRACT

A context sensitive methodology, a Structured Virtual Construct (SVC) system, data tagging techniques, and an apparatus are provided for performing Medical Code-based decision-making involving the matching of a given medical identified element against one or more of a set of known or reference medical identified elements from history or other data elements. A satisfactory decision is achieved as a function of both aggregated ranking (AR) and account adjudication (AA), where account adjudication refers to the full set of values garnered by the Medical Code accumulation process in the process of generating approval/denial/re-classification/of medical diagnosis and/or claim events.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/406,672, filed Oct. 26, 2010, which is incorporated herein byreference in its entirety.

BACKGROUND OF THE INVENTION

Field of the Invention

Methodologies, systems, and apparatuses for performing MedicalCode-based decision-making related to matching a given medicalidentified element against one or more of a set of known or referencemedical identified elements is disclosed herein.

Brief Description of Related Art

Fraudulent and erroneous medical claims are a serious problem, causingan estimated tens of billions of dollars in annual losses to insurancecompanies and governmental agencies.

The current system of medical claims processing, management, payment andreconciliation involves multiple stages of manual processes andworkflows which are augmented by automated accounting and documentationsystems. However, individual insurance companies maintain a vast numberof clinicians and disparate automated systems which are prone to varyingdegrees of limitations inherent to human dependent systems run buyindependent companies. The deficiencies within the current systems hascreated an industry of commercial insurance and government RecoveryAudit Contractors (RAC's), whose payments are based on similar humanreview by clinicians and legal experts whose ultimate benefit is thesharing of recovered payment.

Given this fact and the natural incentive for RAC's to focus on highcost claims, there remains a significant number of un-recouped improperpayments made, which now exceeds $24 billion per year for CMS claimsalone as based on the Office of Management and Budget (OMB) estimates.

SUMMARY OF THE INVENTION

A rule-based method utilizing neural computational logic, statisticallymotivated algorithms and a computationally efficient artificialintelligence management approach to nonlinear dimensionality reductionof options that has form, fit, and functionality preserving propertiesand connection to clustering for representation of high-dimensional datafor performing Medical Code-based decision-making is disclosed, whereinsaid method comprises generating a set of Medical Code Options based onpreliminary information regarding an initial assertion, and applying arule set to each Medical Code option to generate a result of: (1)accumulate and aggregate further information to apply to a Medical Codeoption, (2) render an automatic reclassification of a medical codeoption, (3) generate an auto-accept decision for a medical code option,and (4) generate auto-deny.

Various systems for performing the same are also disclosed. It is to beunderstood that both the foregoing general description and the followingdetailed description are exemplary and explanatory only, and are notrestrictive of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block architecture diagram of a system for performingMedical Code-based decision-making according to one aspect of thepresently disclosed methods, systems, and apparatuses.

The symbol

indicates that a computation involves a financial concept. The symbol

indicates that a computation involves a transaction model construct. Thesymbol

indicates linkage of one or more computations. The area of the FIGURErepresented by Bar (A) indicates transactions involving Medical ClaimActivity Monitoring. The area of the FIGURE represented by Bar (B)indicates transactions involving Application and analytic processing.The section indicated by (C) indicates Transaction / Presentationlayers, including Finance and Accounting Specifics, such as Mandatoryversus Optional; financial elements versus attributes; Automated versusmanual data-entry and masking, workflow routing and packaging, andservice parameters. The section indicated by (D) indicates Authorizationand Security layers, including Role-Process Identification; Standards &Framework Adoption; Qualifier to Object Breakout; Thesaurus Assignment;and Transaction Interchange Mapping. The section indicated by (E)indicates Technology Requirements layers, including Medical Claim Rules/ Patterns; Atomics & Alerts; Structure: Resolution / Adjudication;Workflow / Process identification; Mandatory versus Optional; andSub-set Application Layer. The section indicated by (F) indicatesSemantics and Logic layers, including Medical Claim, Temporal, SpatialContext; Use Case and Work-Flow Sequence; Authoritative Sources; MedicalClaim Activity and Reporting; Concepts Registration;Classification/Categorization Assignment; and Ontology/TaxonomyPlacement.

DETAILED DESCRIPTION OF THE INVENTION

This presently disclosed methods, systems, and apparatuses employ anArtificial Intelligence based apparitions managed within a NeuralNetwork of varying automated elements, which automatically review eachmedical claim and billing for the purpose of replacing human processesrelated to expert medical analysis, authorization, rejection, orre-classification (re-price) billings which do not meet the existingguidelines for payment. These methods, systems, and apparatuses arefurther designed to develop and report metrics which classify improperpayments on claims for services that do not meet Medicare's medicalnecessity criteria; for services that are incorrectly coded; for claimsthat should have been paid by a different health insurance company orMedicare Secondary Payer (MSP); for claims related to outdated feeschedules; and for claims made twice (or more) because duplicate(multiple) claims were submitted. The designed ingest of historical dataand the analysis of the report metrics are used to employ a system ofmachine based automated continuous self learning.

In an aspect, a methodology, a system, and an apparatus for performingMedical Code-based decision-making related to matching a given medicalidentified element against one or more of a set of known or referencemedical identified elements is disclosed herein. A satisfactory decisionis achieved as a function of both aggregated ranking and accountadjudication, where account adjudication, although technicallydescribing just one element in the approval value set, refers to thefull set of values garnered by the Medical Code accumulation process inthe process of generating approval/denial/re-classification/medicalclaim events. Aggregated ranking is a mechanism to set the various“correlation adjudication values,” where the thresholds may be setwithin the system to define acceptable adjudication parameters fordecision-making. The Medical Code is computed on the basis of partialmatching of feature vector elements, where separate and distinct featurevectors are associated with both the given medical identified elementand each of the reference medical identified elements. Feature vectorelement values are used to support the Rules Engine and assist in thedecision-making process. Following Medical Code-combination methods(e.g., those used in Least Squares Fit for linear analysis, Monte Carlocomputational sampling, Markov chain discrete steps and Dempster-Shaferformalisms), the Medical Code is accrued for both the approval anddenial decisions regarding a potential match.

In an aspect, the methodologies, systems, and apparatuses disclosedherein apply statically based analysis in conjunction with the specificapplication of algorithms relating plausibility and potentiality ofaccuracy to form the system baselines and the dynamic update of thosebaselines for determining whether a given medical code is accurate.

In another aspect, a context sensitive methodology, a Structured VirtualConstruct (SVC) system, data tagging techniques, and an apparatus areprovided for performing Medical Code-based decision-making involving thematching of a given medical identified element against one or more of aset of known or reference medical identified elements from history orother data elements is disclosed. A satisfactory decision may beachieved as a function of both aggregated ranking (AR) and accountadjudication (AA), where account adjudication refers to the full set ofvalues garnered by the Medical Code accumulation process in the processof generating approval/denial/re-classification/ of medical diagnosisand/or claim events. Aggregated ranking is a mechanism to set thevarious correlation adjudication values, where the thresholds defineacceptable adjudication parameters for decision-making Medical Codeaggregated ranking is computed on the basis of partial matching offeature vector elements, where separate and distinct feature vectors areassociated with both the given medical identified element and each ofthe reference medical identified elements. Following MedicalCode-combination methods, diagnosis, or symptom descriptivism, MedicalCodes are accrued for both the approval and denial decisions regarding apotential match. Subsequent Medical Code event (MCE) profiles are usedto track medical outcomes and trends. Human Feedback may further beemployed to enhance automated system learning.

The presently disclosed methods, systems, and apparatuses are useful,for example, in decision-making situations where it is preferable togenerate a large number of Medical Code Options, and authorize,reclassify and/or deny these Medical Code Options dynamically.

As used herein, the phrases “reclassify”, “auto-reclassify”,“auto-reclassification”, and “automatically reclassify” shall all referto a command to change a target medical code to a different, moreappropriate code.

As used herein, the phrase “authorize”, “accept”, “auto-accept”, and“automatically accept” shall refer to a command to accept a givenmedical code as probably valid to a degree of confidence as defined bythe rule set.

As used herein, the phrase “auto-deny” shall refer to a command to denya given medical code as probably invalid to a degree of confidence asdefined by the rule set.

The presently disclosed methods, systems, and apparatuses are alsouseful for those cases where it is important to minimize false denials.Historically a large number of initial “false approvals” are tolerated(followed by subsequent more detailed analysis and determinations forcost recovery), with many “false approvals” at risk cost thresholdsbeing allowed to avoid adjudication. Examples of such cases include thedaily execution and adjudication of doctors, hospitals, Medicare orMedicaid claims processing, medical profile screening for complexsurgery, etc.

Similarly, the presently disclosed methods, systems, and apparatuses areuseful when a large number of possible determinations or associationscan be made regarding an medical identified element, e.g., determiningwhich Provider, Patient, or reference medical identified element isreferred to when a provider's name, a person's name, or an extractedmedical identified element is taken from some Medical Record, claimsdocument or other data source. Here, there is again a need to post manypossible alternative matches; e.g., initially to all reference medicalidentified elements which have matching or even similar names,nomenclature, or components. A portion of the objectives are to advancemultiple candidate Medical Code Options across each coding discipline(Durable Medical Equipment (DME), Medicare Parts A-D, Hospice, etc.) andto approve or deny each, until a very small number (preferably a singleMedical Service code) remains as the most appropriate match given fullconsideration to governance and regulation.

The process of approving (validating), reclassifying (refuting andre-calculating) or denying (refuting) any number of Medical Code Optionsis impacted by several factors. This is not a linear classificationtask. In a classification task, the number of particular classes istypically well-known, and typically much smaller than the number ofcandidate medical identified elements that would be matched to a givenclass type. Because classes can be described by combinations of“characteristic attributes,” classification tasks can typically beperformed by one of a number of well-known methods, e.g., statisticalclassifiers, neural networks, etc. However, the presently disclosedmethods, systems, and apparatuses address the case of matchingparticulars—e.g., a given extracted medical identified element (e.g., aperson's name and medical event code) against a large set of referencemedical identified elements (for example, ICD-9 or CPT codes) and/orindividual patient history (e.g., historical Common Working File (CWF)of known persons and medical history). In this case, each referencemedical identified element is characterized uniquely by a set ofparticulars, not as a member of a given class. This means that amethodology other than that typically employed for classification taskswill be required.

As used herein, the phrase “medical code” shall refer to any system usedto document a medical event and/or to bill that medical event to aninsurance company, government payer, or other institution or personcompiling information regarding the medical event. By way of example andnot limitation, the World Health Organization publishes TheInternational Statistical Classification of Diseases and Related HealthProblems (most commonly known by the abbreviation ICD), which is amedical classification that provides codes to classify diseases and awide variety of signs, symptoms, abnormal findings, complaints, socialcircumstances, and external causes of injury or disease. Under thissystem, every health condition can be assigned to a unique category andgiven a code up to six characters long. The ICD system is is used bymost insurance companies and government agencies worldwide. In thetypical case, a level of medical service, once determined by qualifiedstaff, is translated into a standardized five digit procedure code drawnfrom the Current Procedural Terminology (CPT) code set which maintainedby the American Medical Association through the CPT Editorial Paneldatabase. The verbal diagnosis is translated into a numerical code setforth by the ICD. These two codes, a CPT and an ICD-9-CM (will bereplaced by ICD-10-CM as of Oct. 1, 2013) are equally important forclaims processing.

As used herein, the phrase “medical identified element” shall refer toany information associated with a given medical event.

As used herein, the phrase “medical code identified element” shall referto any information associated with a medical code in a given medicalclaim, including but not limited to a medical code, a symptom, adiagnosis, an indication, a test result, or any other information usedto support the assignment of the medical code to the medical claim.

As used herein, the phrase “Provider identified element” shall refer toan identified element associated with a given medical care provider. Byway of example and not limitation, this includes Medicare enrollmentinformation for providers, physicians, non-physician practitioners, andother suppliers. CMS has established standards for information andmaintains that information within its Provider Enrollment, Chain andOwnership System (PECOS) as an alternative to the paper (CMS-855)enrollment process. PECOS is designed to allow physicians, non-physicianpractitioners and provider and supplier organizations to enroll, make achange in their Medicare enrollment, view their Medicare enrollmentinformation on file with Medicare, or check on status of a Medicareenrollment application. In an aspect, the provider identified elementsare selected from elements required by PECOS, which may be augmentedwith other legal information and association elements not required formaintenance by the government. By way of example and not limitation, theprovider identified elements may be obtained from information compiledfrom PECOS.

As used herein, the phrase “Patient identified element” shall refer toan identified element associated with a given patient. By way of exampleand not limitation, the patient identified element may include:demographic information including, but not limited to, the patient'sname, address, social security number, home telephone number, worktelephone number and their insurance policy identity number; guarantorinformation of a parent or an adult related to the patient; militarystatus, such as whether the patient is a veteran active duty military,or reservist; racial or ethnic information; a summary of treatment,including but not limited to one or more diagnoses, and/or the reasonfor the visit, the nature of the illness, examination details,medication lists, diagnoses, and suggested treatment.

As used herein, the phrases “reference identified element” shall referto an identified element in a set of identified elements associated withhistorical medical claims, against which an extracted medical identifiedelement may be compared to determine a probability of whether theextracted identified element is properly associated with the medicalcode and/or claim. The reference identified elements may be drawn from aknowledge base of compiled identified elements associated with variousCodes/Claims, or may be generated dynamically from one or more externaldata sources.

As used herein, the phrase “Rules Set” shall refer to a pre-programmedset of rules applicable to content or appliances, to follow fordetermining whether a given medical identified element supports orrefutes a proposition that a given medical claim should be approved,disapproved, or reclassified. By way of example and not limitation,guidelines for approving or denying claims based on ICD codes are wellknown in the art and publically available. Such rules are generallyapplied by clinicians and medical claims experts and may be applied inthe presently described methods, systems, and apparatuses.

In an aspect, a set of extracted medical identified elements, extractedprovider identified elements, and extracted patient identified elementsassociated may be compared to a set of reference medical identifiedelements associated with the same or similar codes using an appropriaterule set, until a sufficient confidence can be reached regarding whetheran acceptable code decision can be made to accept, reject, orautomatically reclassify a given claim. In a further aspect, the processproceeds iteratively, with each extracted medical identified elementbeing compared against the set of reference medical identified elementsto generate a confidence level regarding the correctness orincorrectness of a given decision until a confidence threshold for anacceptable code decision has been reached.

In a further aspect, all the thresholds for making an acceptabledecision are context-dependent. Frequently, there will be costsassociated with reaching certain levels of adjudication parameter orapproval in a given decision. Depending on the quality of availabledata, the number of competing Medical Code Options, the extent to whichthese Medical Code Options can be readily distinguished from each otherbased on the readily available data, and other factors, it may at timesbe more “costly” to achieve greater adjudication parameters in either orboth validating and/or refuting any of the set of potential Medical CodeOptions. It may at times be possible to deny certain critical MedicalCode Options more readily than it may be possible to authorize,reclassify a approval assertion. In all of these cases, the question ofsetting thresholds for an acceptable decision becomes paramount, as costfactors can rise rapidly as decision adjudication parameter requirementsare increased. Thus, it is useful to have a means for makingcontext-dependent thresholds for “acceptable decisions.” In this manner,review by clinicians, administrators and legal review is replaced by thedescribed methods, reducing overall review time from days and weeks tonano-seconds.

The Medical Service approval/deny methods may comprise at least aMedical Code accumulation method or system for incrementally aggregatinginformation until a satisfactory decision can be reached, where thismethod should yield both degrees of approval and denial for any givenMedical Service, as well as to indicate when an aggregated Medical Codecombination can authorize, reclassify or deny a given Medical Service,or map “ Rules Conflict” related the specific Medical Service.

An ability to deal with partially complete and/or partially erroneousMedical Code, as part of the Medical Code accumulation method, bothassociated with the extracted medical identified element and also withthe reference medical identified elements to which the extracted medicalidentified element will be compared for Medical Service resolution.

A Medical Code selection method or system for selecting the“reclassification” type of Medical Code to both access and aggregate inorder to form the next step of aggregated Medical Code that serves toeither authorize, reclassify or deny a Medical Service, where the meansfor such selection needs to address both the potential “maximalusefulness” that a given piece of Medical Code could provide as well asits potential cost, together with the likelihood that even if acquired,it could possibly be erroneous or incomplete.

As one example, the presently disclosed methods, systems, andapparatuses address the case where a medical identified element (person,organization, place, object, medical code event, etc) is extracted fromtext-based data sources. There are already many methods and capabilitiesfor performing this task, and for purposes of the presently disclosedmethods, systems, and apparatuses, they will be assumed to provide anacceptable set of extracted identified elements which may furtheralready be identified, using one or more of several means known topractitioners of the art, as being a person, place, thing, etc. Further,without loss of generality, the presently disclosed methods, systems,and apparatuses may be independent of the data source from which theidentified elements are extracted; the sourcing data may be structuredor unstructured. In the case of structured data, there is often a set offeature vector elements associated with the identified element; e.g., inthe case of a list of persons, there can also be associated informationsuch as address and phone number. In the case of unstructured data, itis also often possible to create a “context vector” containing bothwords and other extracted medical identified elements which can beuseful for identifying a given extracted medical identified element inthe context of either or both a situation or reference frame as well asother specific extracted medical identified elements.

The presently disclosed methods, systems, and apparatuses defines anacceptable methodology for accumulating Medical Code with regard todecision-making corresponding to a particular assertion, e.g., medicalidentified element matching. The challenges which one aspect of thepresent presently disclosed methods, systems, and apparatuses addressesare those decision-making situations where it is substantiallypreferable to generate a large number of Medical Code Options, and both“authorize, reclassify” and “deny” these Medical Code Options, until afinal decision can be made. The presently disclosed methods, systems,and apparatuses are particularly useful for those cases where it isexceptionally important to minimize “false denials.” Indeed, in manycircumstances, a large number of initial “false approvals” can betolerated (followed by subsequent more detailed analysis anddeterminations), rather than allow any “false denials” to escape.Examples of such cases include security screening for passengers on anaircraft, medical profile screening such as cancer cell/tumor detection,etc.

The presently disclosed methods, systems, and apparatuses address thechallenges previously identified with a decision-making methodology,architecture, and system that includes at least three components ofpresently disclosed methods, systems, and apparatuses: (1) a system forgenerating multiple candidate Medical Code Options, each of which are tobe authorize, reclassify and/or deny, until minimally few Medical CodeOptions remain as viable candidates, (2) a system for determiningcontext-based Medical Code accumulation thresholds corresponding to“acceptable decisions” regarding candidate Medical Code Options, alongwith (3) a system for Medical Code selection and aggregation, in supportof the Medical Code Options approval and deny tasks.

The means for generating candidate Medical Code Options is largelygoverned by the particular situation to which the decision-makingmethod, architecture, and system will be applied.

The goal of Medical Service approval is typically to provide sufficientMedical Code to approve or make a given assertion. One application ofthe disclosed methodologies, systems, and apparatuses thus is todetermine a correct association between an “extracted medical identifiedelement” and one out of a set of “reference medical identifiedelements.” This process will typically continue until an approval matchhas been made.

Illustrative, although by no means limiting, examples include thefollowing: A person solicits and receives Medical Services for a kidneytransplant procedure and the medical providers (doctors, nurses,hospital, staff etc.) gives certain identifying information itemizingeach element (the “extracted medical identified elements”) for billingpurposes. Though each individual element may be properly identified,coded and priced, the governance protocol for the insurance company orgovernment payer may have a fixed or negotiated price for the completeprocedure. The presently disclosed methods, systems, and apparatuses aredesigned to sufficiently match the itemized elements to the operablereferenced medical identified element beginning with the properidentification of the individual and their benefits and ending with thefull review of each procedure code.

Once a Provider or Patient has been associated to some known referencemedical identified element (e.g., using as an example the Center forMedicare and Medicaid's CWF file or one or more commercial datasources), the same person must be confirmed as not likely being on a“Fraud or un-approved vender/patient list?” To this end, a MedicalService deny function is provided to minimize the number of falsedenials resulting from making a given assertion, e.g., with regard toextracted medical identified element evaluation. Such a function may uselist matching may be used. For example, the person or vendor may bescreened against: (i) known Fraud List persons, and/or (ii) theirpotential for “non-obvious relationships”, for example, multiple drugtransactions from disparate Providers to the same person, multiple drugtransactions from a single Provider to the same person over and above anauthorizable dosage and/or multiple drug transactions from a singleProvider to the same person, processed by multiple disparate vendors. Assuch, the system, methods, and apparatuses may be configured to refutinga match between an extracted medical identified element and a keyreference medical identified element, which is useful in determining thesignificance of material associated with the extracted medicalidentified element.

The case of Medical Service deny (e.g., of List matching to known andtrusted sources) may use advancing match Medical Code Options tomultiple candidates drawn from some reference list of persons (from, forexample, agency or company eligibility registry, Dun and Bradstreet,Lexus Nexus, etc. or government and commercial issued Fraud List or“access denied” list). Such a method is useful to advance a large numberof candidate Medical Code Options, thereby generating a very large poolof potential “approval candidates,” and then to deny these matches.

The decision-making processes here are typically of two interwoventypes. Both the tasks of Medical Service approval and Medical Servicedeny require a Medical Code-aggregation and evaluation methodology andsystem. This methodology and system should produce sufficient MedicalCode that matches to known reference content with some degree offidelity.

The difference between Medical Service approval and Medical Service denyis that the goal of Medical Service approval is to garner sufficientMedical Code to support a given (medical identified element-verifying)match. The process should involve not only gathering evidential support,but also (i) ensuring that there are no substantive denials in theproposed verification, and (ii) there are no significant conflictsintroduced by matches to other Medical Code, Provider, or Patientreferenced in the data sources. The process of Medical Service Codedenial similarly garners sufficient Medical Code to support denial in amatch, preferably with minimal approval and conflict.

Thus, the presently disclosed methods, systems, and apparatuses define amethod, architecture, and system by which context-dependent criticaldecision thresholds can be achieved, i.e., formulating a means by whichcritical decision thresholds can be modified as a set of one or moreexternal events or parameters, thus yielding context-dependent decisioncriteria, as a function of both account adjudication and aggregatedranking, which are defined herein.

In order to fulfill the related goals of Medical Service approval anddeny, Medical Code needs to be gathered and aggregated in support ofeach candidate Medical Service.

The approach taken in the presently disclosed methods, systems, andapparatuses addresses those cases where various “medical identifiedelements” can be described by means of feature vectors, where thefeature vectors can take on population of additional, pre-specified datafields as need for additional Medical Code arises. Note that not allfields are filled at the beginning of the decision-making task, nor mustall the fields necessarily be filled in order for a satisfactorydecision to be reached. Additionally, the decision process is influencedthrough a Feedback Loop via human adjudication of reconciledCode/Claims.

The tasks to which this methodology will be applied will involve thefollowing constraints: The a priori probability distribution of thefeature vector values may be unknown, and a priori dependence of theextracted medical identified element association to a referencemedically allowable identified element, resting on a given set offeature vector values, may be difficult to compute, and the number ofpossible competing Medical Code Options, and the effort needed todistinguish an acceptable match to a specific Medical Service (verifyingone Medical Service and refuting others), can be very difficult tospecify in advance.

Further, the following objectives may be satisfied such as Medical Codeaccumulation should be traceable, different kinds of Medical Code can beassociated with both the extracted medical identified elements and thereference medical identified elements, so that no single “path” forMedical Code matching will always be required to be available. Incircumstances where multiple Medical Code combinations may not only bepossible but necessary, Medical Code items (feature vector elements)associated with either or both the extracted medical identified elementsand/or the reference medical identified elements may be partial orincomplete, and yet partial matches between these Medical Code itemsshould contribute some degree of Medical Code accumulation, bothre-classification and conflict in potential decisions/assignments shouldbe represented explicitly, there should be a defined means foraccumulating additional Medical Code to support potential assertions, sothat a “minimal-cost” set of rules for obtaining Medical Code can beapplied (assuming that each “Medical Code unit” carries an associatedcost), and there should be a means to cut-off further Medical Codeaccrual after the sufficient Medical Code has been obtained to support agiven assertion, while the re-classification and/or conflict about thisassertion are within acceptable and defined limits.

The presently disclosed methods, systems, and apparatuses postulate arule-based decision-making method for generating Medical Code Optionsbased on preliminary information that can be ascertained either withregard to the initial assertion(s), and then gathering sufficientMedical Code to deny the majority of the Medical Code Options, whilesimultaneously approving, reclassifying or “validating” the MedicalService that is most correct.

As used herein, the phrase “rules based decision making” shall refer tothe application of statics, algorithms, temporal and geospatialassociations, and rules in rendering an appropriate code decision.

In the typical case, once the a medical procedure and diagnosis codesfor a given claim are determined, the medical provider will document thedecision and the biller will transmit the claim to the insurance company(payer). This is usually done electronically by formatting the claim asan ANSI 837 file and using Electronic Data Interchange to submit theclaim file to the payer directly or via a clearinghouse. Historically,claims were submitted using a paper form; in the case of professional(non-hospital) services and for most payers the CMS-1500 form or HCFA(Health Care Financing Administration claim form) was commonly used. TheCMS-1500 form is so named for its originator, the Centers for Medicareand Medicaid Services. Currently approximately 30% of medical claims getsent to payers using paper forms which are either manually entered orentered using automated recognition or OCR software. Within two yearsall providers will be required to have automated submission. In thepresent methods, systems, and apparatuses, statically based analysis isused in conjunction with the specific application of algorithms relatingto plausibility and potentiality of accuracy to form the systembaselines and the dynamic update of those baselines. The proximity oftime and place also may play a part in the system adjustments. The rulesbased decision uses functions of the baselines to administer theunderstanding and action of the system.

In an aspect, a Dempster-Shafer algorithm is used. By way of example, aStructured Virtual Construct Dempster-Shafer (SVC-DS) may be used. TheSVC-DS process produces a approval-set output for each iteration of theSVC-DS process (each successive step of pairwise Medical Codeaggregation). This approval-set consists of the various initial andresultant Medical Code valuations (approval, denial, andre-classification, along with account adjudication). In addition, aconflict value can be produced after two or more Medical Code items arecombined. This measures the disparity between the approvals that oneMedical Code source might assert against the denial asserted by adifferent form of Medical Code. Thus, instead of having a scalaradjudication parameter value we have not less than a set of threedistinct values (approval, denial, and re-classification), along withtwo others that are additive combinations of certain distinct values.

The goal of using the SVC-DS method is to provide support for adecision. That decision should rightly be made in the context of severaldifferent factors and with regard to the medical identified element inquestion, potential classifications or matches of the medical identifiedelement, and considerations of both potential past as well as futurehistory. In short, the decision-making process that the SVC-DS methodsupports is not static, rather decisions related to the Feedback Loop ismade in context. It is to this end that the first aspect of thepresently disclosed methods, systems, and apparatuses are specificallyaddressed.

The process of refuting Medical Code Options requires that a measure ofdenial be generated as well as approval in the leading Medical CodeOptions. Also, the leading Medical Code Options should mature into oneswhere there is minimal conflict; i.e., no strong indicators of bothapproval and denial Further, when there are multiple competing MedicalCode Options, and multiple small “Medical Code items” being used to formMedical Code “masses” in support of various Medical Code Options, andwhere there is also high likelihood of partial, incomplete, and evenerroneous “Medical Code items” that will be found and used, there willbe a great deal of ambiguity in the early processing stages. Thus, it isvery reasonable to work with measures of re-classification, supportingboth account adjudication and Medical Reasoning, as much as to work withthe more certain statements regarding approval and denial. For thesereasons, a Dempster-Shafer formalism alone is not always an appropriatestarting place for grounding the methods discussed in the presentlydisclosed methods, systems, and apparatuses.

In an aspect, the SVC model framework is applied to the managingDempster-Shafer formalism stimulus information for node responses by:(1) using a tractable statics and algorithm management for computing themodeled estimate of the most probable stimulus to have generated anobserved single or multiple trained or modeled response, given a priordistribution model over the medical reasoning stimulus; (2) using agaussian approximation to the medical reasoning feature vectordistribution that can be used to quantify the fidelity with whichvarious stimulus features are encoded; (3) using a method for estimatingthe mutual information between the medical reasoning feature vectordistribution and spike trains emitted by a neural cluster; and (4) usinga framework for the detection of medical reasoning feature vectordistribution change events or times (the time at which the medicalreasoning feature vector distribution undergoes a change in mean orvariance) by establishing limits and boundaries over the medicalreasoning feature vector distribution.

This combination of support elements makes the SVC-Dempster-Shafermethod suitable for Medical Code aggregation within the overalldecision-support architecture. The use of the Least Squares Fit methodalong with the SVC-DS allows for linear rules based organization andranking of explicit pairwise combination of approvals, along withmeasures of re-classification and denial in a given assertion.

A challenge in using a method such as SVC-DS in conjunction with theLeast Squares Fit is that the initial approval-set values are readilydefined in an “a priori” sense as are the normal distributions used inassociated cluster based classifications. Much like work with neuralnetworks, which can be trained when there are sufficient examples“spanning” the multiple dimensions for Medical Code combinations, it isreasonably assured of complete Medical Code-space coverage required forgood training. Therefore, means for defining not only initial approval,but also denial and re-classification resulting from any initial MedicalCode form may be applied, prior to the Medical Code aggregation.

Because one of the strengths of the SVC-DS method is that it isintrinsically well-suited for dealing with aggregation of partial orincomplete Medical Code, a method is employed that not only defines therelative initial values of each specific type of Medical Code, and alsothe value of “partial” Medical Code of each type, but also the means bywhich denial is associated with a given Medical Code-type when approvalfalls off. This may be determined prior to aggregating a particularMedical Code type with other (either initial and singular, or previouslyaggregated) Medical Code. The present the presently disclosed methods,systems, and apparatuses described herein address this issue.

While the need for a decision tree governing selection of pairwiseelements for combination can require development of a substantial rulesset to cover all the possible cases for obtaining different Medical Codecombinations, this is actually proven to be an advantage in the sensethat each time a Medical Code-unit is requested from a specific source,it is possible to pre-compute the additional cost. It is also possibleto specify in advance how much a given additional form of Medical Codewill be allowed to contribute to the total approval ranking This meansthat cost/benefit tradeoffs for collecting different forms of MedicalCode from different sources can be assessed, leading to a rules setgoverning Medical Code-gathering.

Aggregated ranking can also be used to address benefit, as an indirectfunction of risk. In this case, the “risk” is associated with theexpense of making a focused effort to reach a particular subgroup ofProviders, Patients, or Payees. In the cases of focused ProviderEnrollment and Management, or collecting of improperly paid debts, it isimportant to assess the exceptional benefit that could result frominvesting greater attention or effort to adjudicate the individual claimbilling for hospitals, physicians, skilled nursing facilities, labs,ambulance companies, and durable medical equipment (DME) suppliers. Allconfigurations are designed to maintain rules and adjudication processeswhich have associated nuances of billing which are processedindividually.

Account adjudication, as used in this approach defines the parametersand certainty that a given assertion is true related to the approval.Additionally the degree to which it is credible that a given assertioncould come from Trusted Knowledge Base information is directlyproportional to degree of accuracy in the account adjudication forapproval re-classification, and denial. The presence of are-classification measure makes it possible to express both accountadjudication and doubt. It is also a way to express what is not knownabout a given Provider, Patient, of Medical Coding situation. This makesit possible to identify and account for Medical Code conflicts; as anexample, when one Medical Code element supports the approval that aclaim is authorized and other Medical Code says that the event shouldhave been bundled and is no longer approved for Medical Services. Whenconflicts increase, the need to gather/assess more Medical Code forverification increases.

According to one aspect of the presently disclosed methods, systems, andapparatuses, the account adjudication concept can be used multiple waysin the approach to medical identified element verification. For example,in medical identified element medically allowable identified elementverification the use establishes that an medical identified element iscorrectly identified in terms of known information that can beassociated with that medical identified element.

In the case of medical identified element matching, high confidencemeans that it is directly associative that a given medical identifiedelement matches to some specific known, reference medical identifiedelement. The difference between this case and the previous is that inthe first, the reference medical identified element is actually the onebeing posited for medically allowable identified element verification,and the verification information can come from multiple disparatesources, which may collectively confirm different attributes associatedwith the medical identified element. The process focuses on accumulatingsufficient adjudication parameter in confirming information provided bypotentially disparate sources against the information provided about themedical identified element whose medically allowable identified elementis being verified. In contrast, medical identified element matchingassumes that the medical identified elements who could be matched (thereference medical identified elements) are largely already known, andthat sufficient already-vetted information is attached to each of theseso that the task is more one of matching the given extracted medicalidentified element (the one being matched) against one of thesereference medical identified elements through the use and extension ofdedicated rules. There would preferably be some attributes or contextassociated with the extracted medical identified element, and therewould preferably be a larger set of attributes associated with eachreference medical identified element. Preferably, also, the attributesand/or context associated with the extracted medical identified elementform a subset of the attributes and/or context associated with thereference medical identified element, to facilitate the matchingprocess.

In an aspect, a rule-based method for performing Medical Code-baseddecision-making on a computer-based system comprising at least oneprocessor is provided, wherein said at least one processor: (a)generates a set of Medical Code Options based on preliminary informationregarding an initial assertion data, and (b) applies a rule set to eachMedical Code option to generate a result selected from the groupconsisting of: (b1)) accumulate and aggregate further information toapply to a Medical Code option, (b2) render an automaticreclassification of a medical code option, (b3) generate an auto-acceptdecision for a medical code option, and (b4) generate auto-deny. AStructured Virtual Construct (SVC) Dempster-Shafer (SVC-DS) output wherethe estimate of “Plausibility and Potentiality” proves to be a goodmeasure of limitation and control of trained data in variety of modelformats, including the ability to tractably perform optimal nonlinearfeature vector reconstruction given the activity of ensembles ofinteracting rules models or some similarly focus function is essentialand may be applied to each medical code option to generate the result.

In an aspect, the initial assertion is related to the method ofutilizing neural computational logic, statistically motivated algorithmsand a computationally efficient artificial intelligence managementapproach to nonlinear dimensionality reduction of options that has form,fit, and functionality preserving properties and connection toclustering for representation of high-dimensional

In one aspect, a system for performing Medical Code-baseddecision-making comprising at least one processor is provided, whereinsaid at least one processor is programmed to perform a set of functionscomprising: (a) a Medical Code processor function configured to match atleast one class of extracted medical identified elements against a setof reference medical identified elements (RMIE); (b) a Medical ProviderCode processor function configured to match an extracted medicalidentified element associated with Provider identification against a setof reference medical Provider identified elements (RMPrIE); (c) aMedical Patient Code processor function configured to match an extractedmedical identified element associated with the Patient Name of Recordagainst a set of reference medical Patient identified elements (RMPaIE);(d) a Medical Code selection processor function configured to define andapply a rule set to the extracted medical identified element and a largeindefinite quantity (LIQ) of reference medical identified elements togenerate a total Medical Code identification function; (e) a MedicalCode threshold processor function configured to generate acontext-dependent threshold for an acceptable code decision; and (f) adecision processor function configured to compare the total Medical Codefunction to the context-dependent threshold to generate a resultselected from the group consisting of: (f1) accumulate and aggregatefurther Medical Code aggregation, (f2) render an automaticreclassification, (f3) generate an auto-accept decision, and (f4)generate auto-deny.

As used herein, the phrase “large indefinite quantity” shall refer toany quantity of information generated dynamically from at least one datasource, such that the quantity of information is sufficient to raise thelevel of confidence regarding a given decision. In an aspect, the “largeindefinite quantity” may be derived from the total number of claimsprocessed by the a large insurance company, the Department of Defense,the Veterans Administration and/or CMS for a given time period. In afurther aspect, the large indefinite quantity is continuously adjusteduntil the confidence is raised above the threshold level.

The set of reference medical identified elements in each of theforegoing systems may be obtained from a Knowledge Base that is storedon computer readable medium, or may be generated dynamically throughreference to databases or other sources. Moreover, each of the RMIE,RMPrIE, and RMPaIE are maintained in the same Knowledge Base orseparately along with NIH maintained Medical and Pharmaceutical termsand definitions, CPT and ICD-9 coding. In an aspect, the systemcomprises a Knowledge Base comprising RMIE, a Knowledge Base comprisingRMPrIE, and/or a Knowledge Base comprising RMPaIE.

The processor may be further programmed to perform a Medical Code eventprocessor function to generate medical code event profiles to trackmedical outcomes and trends. Medical Code event processing inherentlyproduces access to several other important and useful quantities ofinformation including approved diagnosis, treatment plans andprescription drugs each of which may be quantified in a probabilitystructure to which uncertainty is inherent. Simple alerts can be made toboth providers and patients for follow-up (calendar alert for follow-up,e-mail alert for prescription renewal, etc), however history files andreal time patient tracking allows the system to perturb or monitorpatient tracking against established baselines with the potential ofadjustments being made slightly in some direction for some smallpositive or negative scalar made as a result of computing the ratio ofanticipated posterior results at two points (start point and currentpoint extrapolated to current point and forecasted end point orequivalently the difference in the log posterior (forecasted end point).If the posterior in process end points are managed virtually within thesystem changes significantly with the diagnosis, then this change iseasily “detectable”, “forecastable” and highly discriminateable from theprescription or the baseline. Conversely, if the size of the in processposterior points are small and it is difficult to discriminate betweenstatistical baseline and proscribed medical coding. On the basis of thedata one could expect the estimate to be highly variable in thisdirection and a corresponding confidence interval as established by aDempster-Shafer algorithm could serve to limit the significant number ofneedless follow-up visits or extended patient stay. In affect, sickpeople are treated quicker and well people are identified earlier.

The system may further comprise a user interface configured to permithuman feedback to enhance automated system learning. In somecircumstances, Human feedback related to medical claims analysis andprocesses by experienced clinicians, billing, accounting and legalexperts may be desired for various uses of the analytic functionalitydescribed. Additionally, human feedback from patents related toself-monitoring can be used to enhance the baseline of the individualbeyond that of the generalized norm established by the history file. Inthe example of a patient who has had Congestive Heart failure, remotemonitoring or self reporting could include online monitoring orreporting of: a) Blood pressure, Pulse, Weight, and Diet through homedevices and automated logbook of monitored results. Here, human feedbackwill enhance automated learning while empowering the potential ofautomated alerts from machine learning.

The processor may be further programmed to accumulate and aggregate aset of enhanced reference medical identified elements against which eachof the extracted medical identified element will be matched. As usedherein, the term “enhanced reference medical identified elements” refersto a reference medical identified element which has been cross-testedagainst an outside data source. For example, in the case where thereference medical identified element is maintained in a Knowledge Base,a second independent data source may be accessed to provide supplementaldata regarding the reference medical identified element to generate anew set of medical identified elements against which the extractedmedical identified element may be tested. By way of example and notlimitation, the second independent data source may be a claims made tothe Veteran's Administration or through CMS.

The system may further comprise one or more data sources configured toenhancements of the medical identified elements in the form of eitherfeature vector elements and/or corrections to feature vector elementsfor either of the reference medical Provider, Patient, or Codeidentified elements and the extracted medical diagnostic identifiedelements, thereby generating a large indefinite quantity of augmentedfeature vectors for each of the large indefinite quantities of extractedmedical identified elements and/or the reference medical identifiedelements. These enhancements may range from a simple correction in apatient or provider address to the complete addition of the ICD-10 code.The data sources may be any computer-accessible source comprising datarelevant to the extracted medical identified elements. Exemplary datasources include, but are not limited to, data compiled and maintained bythe Veteran's Administration, CMS and other authorized sources. In anaspect, the one or more data sources may be stored on acomputer-readable medium, and/or configured to provide data for: (1) aset of reference Provider identified elements, Patient identifiedelements, and medical identified elements, and (2) a set of enhancedreference medical identified elements configured to augment a largeindefinite quantity of feature vectors associated with each of the largeindefinite quantity of extracted medical identified elements, therebygenerating a large indefinite quantity of augmented feature vectors foreach of the large indefinite quantity of extracted medical identifiedelements.

In most cases it is likely that any assertion posed by the rulesconfiguration formalism will need to withstand queries regarding itsbelievability. Bayesian methods may be used in this area. Alternatively,the Dempster-Shafer (SVC-DS) method may also be used.

The Medical Code selection processor function may further comprise: aMedical Service generator function configured to generate one or moreMedical Code Options about the extracted or referenced medicalidentified element; and a Medical Service approval processor functionconfigured to authorize, reclassify, and/or deny the generated MedicalCode Options to generate a total Medical Code function, wherein thetotal Medical Code function is computed uniquely and distinctively foreach Medical Service regarding a potential match between Provider,Patient, an extracted medical identified element and a reference medicalidentified element.

The Medical code selection processor function may be further configuredto apply a rule set to generate a large indefinite quantity of MedicalCode Options and to gather data to authorize, reclassify or deny thegenerated Medical Code Options.

A threshold processor function is involved in all measurable analyticfunctions and may be further configured to define the context-dependentthreshold for an acceptable code decision by applying an auditable ruleset using an aggregated ranking calculation and account adjudicationcalculation.

The processor may be further programmed to perform: (g) a reconciliationprocessor function configured to: (g1) acquire additional Medical Codeinformation from the one or more data sources, according to the rule setprovided by the Medical Code selection processor function, for theextracted medical identified element and each of its associated linearand non-linear matches to a reference medical identified element orenhanced reference medical identified element; (g2) evaluate the linearand non-linear matches and determine if additional Medical Code isrequired to evaluate the linear and non-linear matches; and (h) a secondMedical Code processor function by which the additional Medical Code isaggregated with the existing Medical Code associated with the extractedmedical identified element, forming an expanded feature vector setuniquely associated with the extracted medical identified elements.

The Medical Code processor function may be further configured toaccumulate Medical Code tracking so that the Medical Codes aretraceable. In an aspect, disparate metadata from various functionalelements within the system and/or from data sources are used to createmetadata silos across the system enterprise. This function allows theconsolidated view of vital metadata relationships comprised in thedecision process of an acceptance, rejection or reclassification to bepreserved. This function of Medical Code Tracking provides an ability toperform impact analysis and to provide the data lineage needed tojustify the system assessment for data usage, end-to-end impactanalysis, and report-to-source data lineage. In an aspect, the functionis designed to serve as a central management tracker. This function alsoserves as the integration point to various external content managementsolutions such as Microsoft Windows, Tomcat, UNIX (Linux, Solaris, andIBM AIX), IBM WebSphere, Weblogic from Oracle BEA, and OracleApplication Server (OAS).

The extracted medical identified elements and reference medicalidentified elements may be configured to comprise a large indefinitequantity of feature vectors. In an aspect, the feature vectors of thereference medical identified elements are fully populated with a largeindefinite quantity of element values and if element values areunfilled, probability value is placed on an adjudication thresholdrelative to the degree and criticality of the non-populated element. Inanother aspect, at least one of the large indefinite quantity of featurevectors comprises a large indefinite quantity of vector elements. Inanother aspect, the Medical Code processor may be configured to obtainadditional element values for the large indefinite quantity of referencemedical identified element feature vectors.

In another aspect, a system for performing Medical Code-baseddecision-making from a set of data elements is provided, the systemcomprising: (a) a large indefinite quantity of extracted medicalidentified elements stored on a computer readable medium, wherein eachextracted medical identified element has associated with it a largeindefinite quantity of feature vectors each having a large indefinitequantity of feature vector elements; (b) a set of reference medicalidentified elements stored on a computer readable medium; (c) a set ofenhanced reference medical identified elements stored on a computerreadable medium; (d) one or more data sources stored on a computerreadable medium, configured to enhancements in the form of eitherfeature vector elements and/or corrections to feature vector elementsfor either or both the reference medical identified elements and theextracted medical identified elements, thereby generating a largeindefinite quantity of augmented feature vectors for each of the largeindefinite quantity of extracted medical identified elements and/or thereference medical identified elements; (e) at least one processorprogrammed to perform: (e1) a Medical Code processor function configuredto compare the large indefinite quantity of augmented feature vectorsassociated with each of the large indefinite quantity of extractedmedical identified elements against a large indefinite quantity offeature vectors for the set of reference medical identified elements orthe set of enhanced reference medical identified elements; and (e2) athreshold processor function configured to generate a context-dependentthreshold for an acceptable decision.

The processor may be further programmed to perform a Medical Codeselection processor function configured to define a large indefinitequantity of rule sets to be applied to each of the large indefinitequantity of extracted medical identified elements and the largeindefinite quantity of reference medical identified elements andconfigured to generate an initial set of extracted medical identifiedelements.

The rule sets may be derived by performing a multidimensional lookuptask and characterizing the large indefinite quantity of vector elementsof the large indefinite quantity of feature vectors against normativevalues. The rules modeling multidimensional lookup serves as a analyticand reporting function used to model, analyze, test and save businessrules as executable decision services. The system supports all aspectsof the modeling process, from initial capture of Medical Code processingrequirements through the testing of the decision against organizationaldata—delivering complete, payment ready decision services. The advancedmultidimensional analysis function for decision logic validation ofcomprehensive medical claims scenario-based models also includetemplate-based reporting for documentation and audit. Business RulesMetrics and Reporting is also used to manage all run-time aspects ofdecision services.

The processor may be further programmed to perform a reconciliationprocessor function, configured to acquire an additional Medical Codefrom the one or more data sources, according to the rule set provided bythe Medical Code selection processor function, for each member of thecandidate approval pool of allowable claims and each of its associatedlinear and non-linear matches to a reference medical identified elementor enhanced reference medical identified element.

The processor may be further programmed to perform a second Medical Codeprocessor function by which the additional Medical Code is aggregatedwith an existing Medical Code associated with a candidate approvalmedical identified element, forming an expanded feature vector setuniquely associated with the candidate approval medical identifiedelement. This function is designed to mimic the clinicians methods ofconsolidating individual codes into a single consolidated code.

The processor may be further programmed to perform a decision processorfunction, configured to apply an evaluation to the new feature vectorassociated with a given candidate approval medical identified element,to further refine the previous decision as to whether the candidateapproval is a definite match, a definite not-match, or requires moreMedical Code identification or analysis for Medical Service adjudicationor deny regarding its linear or non-linear match against a enhancedreference medical identified element or a reference medical identifiedelement.

The Medical Code selection processor function may further comprises aMedical Service generator function configured to generate one or moreMedical Code Options about each of the large indefinite quantity ofextracted medical identified elements; and a Medical Service approvalprocessor function configured to authorize, reclassify, and/or deny thegenerated Medical Code Options to generate the initial set of extractedmedical identified elements. In an aspect, the Medical Code Optionsgenerator function may be configured to apply the rule set to generate alarge indefinite quantity of Medical Code Options, and the Medical Codeprocessor function may be configured to apply the rule set to gatherdata to authorize, reclassify or deny the generated Medical CodeOptions.

The threshold processor function may further be configured to apply therule set to define the context-dependent threshold for an acceptabledecision using an aggregated ranking calculation and accountadjudication calculation.

The set of enhanced reference medical identified elements comprisesvarious permutations of the referenced medical identified elements,wherein the set of enhanced reference medical identified elements islarger than and inclusive of the set of reference medical identifiedelements. In an aspect, the reference medical identified elements andthe enhanced reference medical identified elements are related asdifferent versions of a medical code system, for example ICD 9(reference medical identified elements) and ICD 10 (enhanced referencemedical identified elements).

In another aspect, a system for performing Medical Code-baseddecision-making is provided, the system comprising at least oneprocessor programmed to perform: (a) a Medical Code processor function,configured to compare a large indefinite quantity of augmented featurevectors associated with each of a large indefinite quantity of extractedmedical identified elements against a large indefinite quantity offeature vectors for a set of reference medical identified elements or aset of enhanced reference medical identified elements; and (b) athreshold processor function, configured to generate a context-dependentthreshold for an acceptable decision.

The system may further comprise one or more data sources configured asenhancements of the medical identified elements in the form of eitherfeature vector elements and/or corrections to feature vector elementsfor either of the reference medical Provider, Patient, or Codeidentified elements and the extracted medical diagnostic identifiedelements, thereby generating a large indefinite quantity of augmentedfeature vectors for each of the large indefinite quantities of extractedmedical identified elements and/or the reference medical identifiedelements. The data sources may be any computer-accessible sourcecomprising data relevant to the extracted medical identified elementssuch as those anticipated by the inclusion of ICD-10 coding. Exemplarydata sources include, but are not limited to, data sources maintained bythe Veterans Administration and CMS. In an aspect, the one or more datasources may be stored on a computer-readable medium, and/or configuredto provide data for: (1) a set of reference Provider identifiedelements, Patient identified elements, and medical identified elements,and (2) a set of enhanced reference medical identified elementsconfigured to augment a large indefinite quantity of feature vectorsassociated with each of the large indefinite quantity of extractedmedical identified elements, thereby generating a large indefinitequantity of augmented feature vectors for each of the large indefinitequantity of extracted medical identified elements.

A dynamic representation of Medical Coding and Claims Rules may furtherbe provided, where different nodes are activated to a degree to whichthere is Medical Code supporting their approval as a true state ofaffairs, to serve as a means by which Medical Code is accrued to supportapproval. In some cases, an assertion posed by the rules configurationformalism will need to withstand scrutiny regarding its believability.Bayesian methods may be applied. Alternatively, the Dempster-Shafer(SVC-DS) method may be employed, in which both measures of approval aswell as denial. This method uses the combination of both to serve as amore powerful means for handling Medical Code in support (as well asagainst) an assertion.

The processor may be programmed to perform a Medical Code selectionprocessor function for defining a rule set to be applied to theextracted medical identified element and the large indefinite quantityof reference medical identified elements and configured to generate aninitial set of extracted medical identified elements.

The processor may be programmed to perform a reconciliation processorfunction, configured to acquire additional Medical Code from the one ormore data sources, according to the rule set provided by the MedicalCode selection processor function, for each member of the initial set ofextracted medical identified elements and each of its associated linearand non-linear matches to a reference medical identified element orenhanced reference medical identified element;

The processor may programmed to perform a second Medical Code processorfunction, by which an additional Medical Code is aggregated with anexisting Medical Code associated with the extracted medical identifiedelement, forming an expanded feature vector set uniquely associated withthat extracted medical identified element.

The processor may programmed to perform a decision processor function,configured to apply an evaluation to a new feature vector associatedwith the extracted medical identified element, to further refine aprevious decision as to whether the candidate approval is a definitematch, a definite not-match, or requires more Medical Code for MedicalService deny regarding its linear or non-linear match against a enhancedreference medical identified element or a reference medical identifiedelement.

The Medical Code selection processor function may be further configuredto generate an initial set of extracted medical identified elements by:(1) a Medical Service generator function configured generate one or moreMedical Code Options about the extracted medical identified element; (2)a Medical Provider approval processor function configured to authorize,reclassify or deny a Medical Provider Option generated by the MedicalService generator function; (3) a Medical Patient approval processorconfigured to authorize, reclassify, and/or deny a Patient Medical CodeOption generated by the Medical Service generator function; and (4) aMedical Service approval processor function configured to authorize,reclassify, and/or deny a Medical Code Option generated by the MedicalService generator function.

The rule set may used by the Medical Code Options generator function togenerate a large indefinite quantity of Medical Code Options and by theMedical Code processor function to gather data to authorize, reclassify,or deny the Medical Code Options generated by the Medical Servicegenerator function. In an aspect, the rule set is derived by performinga multidimensional lookup task and characterizing the large indefinitequantity of vector elements of the large indefinite quantity of featurevectors against normative values. In a further aspect, a primaryinformation vector, an activity vector and a context vector is providedfor each extracted medical identified element. The primary informationvector refers to at least one item of personally identifying informationassociated with an extracted medical identified element, such as, forexample, a Patient name; a relationship or potential relationship toanother Patient; or a date of birth. The activity vector may comprise atleast one item of information associated with a treatment course in theextracted medical identified element, such as, for example, treatmenthistory, prescription history and profile information and/or Providerstatement information. The context vector may comprise structured,unstructured, or semi-structured contextual information related to theinformation contained in the activity vector, such as the date, time,and place.

In another aspect, a system for performing automated security screeningusing Provider, Patient and Medical Code-based decision-making isprovided, the system comprising at least one processor programmed toperform: (a) a Medical Code processor function, configured to match anextracted medical identified element against a set of reference medicalidentified elements; (b) a Medical Code selection processor function,configured to (b1) define a rule set to be applied to the extractedmedical identified element and the large indefinite quantity ofreference medical identified elements and (b2) generate a total MedicalCode function; (c) a threshold processor function configured to generatea context-dependent threshold for an acceptable decision; and (d) adecision processor function, configured to compare the total MedicalCode function to the context-dependent threshold and determine whetherto accumulate and aggregate further Medical Code or to generate adecision.

In a further aspect, a primary information vector, an activity vectorand a context vector is provided for each extracted medical identifiedelement. The primary information vector refers to at least one item ofpersonally identifying information associated with an extracted medicalidentified element, such as, for example, a Patient name; a relationshipor potential relationship to another Patient; or a date of birth. Theactivity vector may comprise at least one item of information associatedwith a treatment course in the extracted medical identified element,such as, for example, treatment history, prescription history andprofile information and/or Provider statement information. The contextvector may comprise structured, unstructured, or semi-structuredcontextual information related to the information contained in theactivity vector, such as the date, time, and place.

In another aspect, a system for performing Medical Code-baseddecision-making is provided, the system comprising a processorconfigured to: (a) match an extracted medical identified element againsta set of reference medical identified elements; (b) match an extractedProvider identified elements against a set of reference Provideridentified elements; (c) match an extracted Patient identified elementsagainst a set of reference medical Patient identified elements; (d)define a rule set to be applied to the extracted medical identifiedelement and the large indefinite quantity of reference medicalidentified elements and configured to generate a total Medical Codefunction; (e) generate a context-dependent threshold for an acceptabledecision, wherein the context-dependent threshold is a function of anaggregated ranking value and account adjudication value; and (f) comparethe total Medical Code function to the context-dependent threshold anddetermine whether to accumulate and aggregate further Medical Code or togenerate a decision-result.

The aggregated ranking value may be independent of any information aboutthe extracted medical identified element and is a measure representativeof a need to obtain more information about the extracted medicalidentified element.

The account adjudication value may be a degree to which it is auditable.

The aggregated ranking value may be independent of any information aboutthe extracted medical Provider identified element and is a measurerepresentative of a need to obtain more information about the extractedmedical Provider identified element.

The Patient account adjudication value is a degree to which the claimresult of acceptance, denial or reclassification is auditable and that agiven assertion can be traced.

As will be well understood by a person of ordinary skill in the art,each of the identified functions in the foregoing systems may beperformed by a single processor or a plurality of processors.Additionally, a single processor may be programmed to perform a singleidentified function or a plurality of functions.

EXAMPLES

FIG. 1 is a flow chart of an exemplary method, system, and apparatus asdisclose herein.

The approach is designed to extend the capabilities of Medical Claimsadjudication and review well beyond the existing functionality with bestof breed technology. The system is designed to control data movement andmigration with a novel ability to write it to various targets.Alternatively, as we transition to a dynamic data transfer fromestablished MEDICAL CLIENT legacy data, the system can access andconvert this content through ODBC drivers. This Medical Claim TransitionActivity serves as the first step in the Artificial Intelligencenavigation. Here the Clinical Billing Framework serves as the graphicaluser interface from which we define and manage data maps for relationaland non-relational data sources as we target optional personal metadataprofiles and perform database row tests. By performing a row test on adata map, we can view the source data formatted into our Data Dictionaryfor conversion into objects. (For CMS this includes Imbedded VariableLength record fields). The MEDICAL CLIENT Data Dictionary systemcomponent manages data maps for non-relational files and tables andmaintains them in the DATAMAPS file. This portion of the system handlesbulk data extraction requests from the Analytic Layer for preparationfor Adjudication. If the MEDICAL CLIENT data source or target is on asystem that is remote from the one on which we are using, we willextract bulk data from and load bulk data to the following types ofdatabases and files: Relational databases and flat files on Linux, UNIX,and Windows; DB2 tables and flat files on i5/OS; Relational databases,non-relational databases, and sequential data sets on MVS; Microsoft SQLServer; Oracle; and Sybase tables on Linux, UNIX, or Windows.

The invention claimed is:
 1. A computer-based Artificial Intelligence(AI) method for performing Medical Code-based decision makingcomprising: analyzing, by an AI based computer utilizing neuralcomputational logic, medical and contextually related data, using AI inan iterative context sensitive methodology for performing MedicalCode-based decision-making comprising a matching of a given medicalidentified element against one or more of a set of known or referencemedical identified elements, wherein the AI comprises one or more of aleast squares fit for linear analysis, a Monte Carlo computationalsampling, a Markov chain discrete step and a Dempster-Shafer formalism;and determining, by an AI based computer, an acceptable code decisioncomprising a plurality of iterative decisions including matching thegiven medical identified element with the one or more of a set of knownor reference medical identified elements to optimize a confidence levelof correctness until a non-optimal confidence threshold for anacceptable code decision is reached.
 2. The computer-based AI method ofclaim 1, wherein the Medical Code-based decision-making furthercomprises an aggregated ranking to determine a correlation adjudicationvalue.
 3. The computer-based AI method of claim 2, wherein theaggregated ranking generates a threshold that defines an acceptableadjudication parameter for decision-making.
 4. The computer-based AImethod of claim 1, wherein the acceptable code decision comprisesrendering an automatic reclassification, executing an auto-acceptdecision or executing an auto-deny.
 5. The computer-based AI method ofclaim 1, wherein the context sensitive methodology comprises determininga critical decision threshold that is modified based on a set of one ormore external events or parameters.
 6. The computer-based AI method ofclaim 1, wherein the Al further comprises a user interface configured topermit human feedback to enhance automated system learning.
 7. Anon-transitory computer readable medium with instructions stored thereonto perform Medical Code-based decision making, the decision makingcomprising: analyzing, by an Artificial Intelligence (AI) based computerutilizing neural computational logic, medical and contextually relateddata, using AI in a iterative context sensitive methodology forperforming Medical Code-based decision-making comprising a matching of agiven medical identified element against one or more of a set of knownor reference medical identified elements, wherein the AI comprises oneor more of a least squares fit for linear analysis, a Monte Carlocomputational sampling, a Markov chain discrete step and aDempster-Shafer formalism; and determining, by an AI based computer, anacceptable code decision comprising a plurality of iterative decisionsincluding matching the given medical identified element with the one ormore of a set of known or reference medical identified elements tooptimize a confidence level of correctness until a non-optimalconfidence threshold for an acceptable code decision is reached.
 8. Thenon-transitory computer readable medium of claim 7, wherein the MedicalCode-based decision-making further comprises an aggregated ranking todetermine a correlation adjudication value.
 9. The non-transitorycomputer readable medium of claim 8, wherein the aggregated rankinggenerates a threshold that defines an acceptable adjudication parameterfor decision-making.
 10. The non-transitory computer readable medium ofclaim 7, wherein the acceptable code decision comprises rendering anautomatic reclassification, executing an auto-accept decision orexecuting an auto-deny.
 11. The non-transitory computer readable mediumof claim 7, wherein the context sensitive methodology comprisesdetermining a critical decision threshold that is modified based on aset of one or more external events or parameters.
 12. The non-transitorycomputer readable medium of claim 7, wherein the AI further comprises auser interface configured to permit human feedback to enhance automatedsystem learning.